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Features Importance

Spearman Correlation of Models

Summary of 4_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
3.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.615462 |
nan |
| auc |
0.730194 |
nan |
| f1 |
0.702065 |
0.398721 |
| accuracy |
0.67325 |
0.492145 |
| precision |
0.804054 |
0.747265 |
| recall |
1 |
0.102519 |
| mcc |
0.346372 |
0.492145 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.615462 |
nan |
| auc |
0.730194 |
nan |
| f1 |
0.667479 |
0.492145 |
| accuracy |
0.67325 |
0.492145 |
| precision |
0.662636 |
0.492145 |
| recall |
0.672393 |
0.492145 |
| mcc |
0.346372 |
0.492145 |
Confusion matrix (at threshold=0.492145)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1154 |
558 |
| Labeled as 1 |
534 |
1096 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 3_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
33.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.620592 |
nan |
| auc |
0.721372 |
nan |
| f1 |
0.698278 |
0.287711 |
| accuracy |
0.667265 |
0.45093 |
| precision |
0.77551 |
0.821216 |
| recall |
1 |
0.019132 |
| mcc |
0.339974 |
0.428066 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.620592 |
nan |
| auc |
0.721372 |
nan |
| f1 |
0.67712 |
0.45093 |
| accuracy |
0.667265 |
0.45093 |
| precision |
0.642778 |
0.45093 |
| recall |
0.715337 |
0.45093 |
| mcc |
0.337971 |
0.45093 |
Confusion matrix (at threshold=0.45093)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1064 |
648 |
| Labeled as 1 |
464 |
1166 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.692846 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.655672 |
0.439048 |
| accuracy |
0.487732 |
0.439048 |
| precision |
0.487732 |
0.439048 |
| recall |
1 |
0.439048 |
| mcc |
0 |
0.439048 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.692846 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.655672 |
0.439048 |
| accuracy |
0.487732 |
0.439048 |
| precision |
0.487732 |
0.439048 |
| recall |
1 |
0.439048 |
| mcc |
0 |
0.439048 |
Confusion matrix (at threshold=0.439048)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
1712 |
| Labeled as 1 |
0 |
1630 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 4_Default_NeuralNetwork |
1 |
Metric details
|
score |
threshold |
| logloss |
0.615462 |
nan |
| auc |
0.730194 |
nan |
| f1 |
0.702065 |
0.398721 |
| accuracy |
0.67325 |
0.492145 |
| precision |
0.804054 |
0.747265 |
| recall |
1 |
0.102519 |
| mcc |
0.346372 |
0.492145 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.615462 |
nan |
| auc |
0.730194 |
nan |
| f1 |
0.667479 |
0.492145 |
| accuracy |
0.67325 |
0.492145 |
| precision |
0.662636 |
0.492145 |
| recall |
0.672393 |
0.492145 |
| mcc |
0.346372 |
0.492145 |
Confusion matrix (at threshold=0.492145)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1154 |
558 |
| Labeled as 1 |
534 |
1096 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
7.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.636107 |
nan |
| auc |
0.686825 |
nan |
| f1 |
0.685212 |
0.360543 |
| accuracy |
0.645123 |
0.393044 |
| precision |
0.688312 |
0.64668 |
| recall |
1 |
0.119135 |
| mcc |
0.290289 |
0.393044 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.636107 |
nan |
| auc |
0.686825 |
nan |
| f1 |
0.640388 |
0.393044 |
| accuracy |
0.645123 |
0.393044 |
| precision |
0.633094 |
0.393044 |
| recall |
0.647853 |
0.393044 |
| mcc |
0.290289 |
0.393044 |
Confusion matrix (at threshold=0.393044)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1100 |
612 |
| Labeled as 1 |
574 |
1056 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 5_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
12.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.614998 |
nan |
| auc |
0.720098 |
nan |
| f1 |
0.697663 |
0.339689 |
| accuracy |
0.663674 |
0.463685 |
| precision |
0.782828 |
0.756028 |
| recall |
1 |
0.0893927 |
| mcc |
0.332139 |
0.435307 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.614998 |
nan |
| auc |
0.720098 |
nan |
| f1 |
0.669217 |
0.463685 |
| accuracy |
0.663674 |
0.463685 |
| precision |
0.6431 |
0.463685 |
| recall |
0.697546 |
0.463685 |
| mcc |
0.329428 |
0.463685 |
Confusion matrix (at threshold=0.463685)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1081 |
631 |
| Labeled as 1 |
493 |
1137 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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